Group for Research in Decision Analysis

Stochastic modeling and control of aggregated electric loads: Using demand response to deliver load following and regulation

Duncan Callaway University of Michigan, United States

This talk presents new methods to model and control the aggregated power demand from a population of thermostatically controlled loads, with the goal of delivering services such as regulation and load following. Previous work on direct load control focuses primarily on peak load shaving by interrupting power to loads. In contrast, the emphasis of this work is on controlling loads to produce relatively short time scale responses (hourly to sub-hourly), and the control signal is applied by manipulation of temperature set points, possibly via programmable communicating thermostats. To this end, the methods developed here leverage the existence of system diversity and use physically-based load models to inform the development of a new theoretical model that accurately predicts - even when the system is not in equilibrium - changes in load resulting from changes in thermostat temperature set points. Insight into the transient dynamics that result from set point changes is developed by deriving a new exact solution to a well-known aggregated load model. The talk will also touch on the issue of population heterogeneity and finds that it has a positive effect on model accuracy. Finally, the effectiveness of a simple control law is demonstrated in simulations wherein a population of loads is made to follow the output of a wind plant with very small changes in the nominal thermostat temperature set points.